WO2001033547B1 - Methods and apparatuses for signal analysis - Google Patents

Methods and apparatuses for signal analysis

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Publication number
WO2001033547B1
WO2001033547B1 PCT/NL2000/000808 NL0000808W WO0133547B1 WO 2001033547 B1 WO2001033547 B1 WO 2001033547B1 NL 0000808 W NL0000808 W NL 0000808W WO 0133547 B1 WO0133547 B1 WO 0133547B1
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WIPO (PCT)
Prior art keywords
signal
segment
input
time
correlator
Prior art date
Application number
PCT/NL2000/000808
Other languages
French (fr)
Other versions
WO2001033547A1 (en
Inventor
Tjeerd Catharinus Andringa
Hendrikus Duifhuis
Hengel Pieter Willem Jan Van
Michael Gerardus Heemskerk
Maartje Marjolein Nillesen
Original Assignee
Huq Speech Technologies B V
Tjeerd Catharinus Andringa
Hendrikus Duifhuis
Hengel Pieter Willem Jan Van
Michael Gerardus Heemskerk
Maartje Marjolein Nillesen
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by Huq Speech Technologies B V, Tjeerd Catharinus Andringa, Hendrikus Duifhuis, Hengel Pieter Willem Jan Van, Michael Gerardus Heemskerk, Maartje Marjolein Nillesen filed Critical Huq Speech Technologies B V
Priority to US10/129,460 priority Critical patent/US6745155B1/en
Priority to CA2390244A priority patent/CA2390244C/en
Priority to DE60033549T priority patent/DE60033549T2/en
Priority to EP00980108A priority patent/EP1228502B1/en
Priority to JP2001535156A priority patent/JP4566493B2/en
Priority to AU17408/01A priority patent/AU1740801A/en
Publication of WO2001033547A1 publication Critical patent/WO2001033547A1/en
Publication of WO2001033547B1 publication Critical patent/WO2001033547B1/en

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    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/90Pitch determination of speech signals
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R23/00Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
    • G01R23/16Spectrum analysis; Fourier analysis
    • G01R23/175Spectrum analysis; Fourier analysis by delay means, e.g. tapped delay lines
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208Noise filtering

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Signal Processing (AREA)
  • Computational Linguistics (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Quality & Reliability (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)
  • Analysing Materials By The Use Of Radiation (AREA)
  • Complex Calculations (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

A basilar membrane model is used to receive an input signal including a target signal in step I. With successive further steps the target signal is filtered from the input signal. After the filtering the target signal can be used as an input for further processing, like for example signal recognition of data compression. The target signal can also be applied to a substantially reverse method to obtain an improved or clean signal.

Claims

- 90 -AMENDED CLAIMS[received by the International Bureau on 11 May 2001 (11.05.01); original claims 1-37 replaced by new claims 1-53 (15 pages)]
1. An apparatus for estimating frequency characteristics of an input signal, including: a basilar membrane model device to which the signal is applied and a correlator device connected to the basilar membrane model device, said correlator device having: a first input connected to a segment of the basilar membrane model, for receiving a BM (basilar membrane) signal which stems from the segment, which BM signal is present for a predetermined period of time; and at least one second input connected to the same segment of the basilar membrane model device, for receiving the BM signal shifted over an adjustable time shift Tl, and which correlator device provides a time shift Tl_dependent output signal which is further dependent on frequencies substantially present in the BM signal of the segment and which output signal forms a measure for the frequency content of the signal.
2. An apparatus as claimed in claim 1, wherein said adjustable time shift Tl is adjusted to correspond substantially to the inverse of the characteristic frequency of the segment.
»
3. « An apparatus as claimed in claim 1 or 2, wherein a multiple of segments of the basilar membrane model are each connected to a separate correlator device , and wherein the apparatus further includes: a cross-correlator device connected to the respective outputs of at least a number of the correlator devices for determining common periods predominantly present in the BM signals of the segments. - 91 -
4. An apparatus as claimed in any one of the preceding claims, wherein said correlator device is implemented as a leaky autocorrelator device arranged for performing an operation represented by the mathematical algorithm:
, wherein r is the output signal of the correlator device, X is the BM signal, s is the segment position, t is the time, T is the adjustable time shift and L is a low-pass filtering method.
5. An apparatus as claimed in any one of the preceding claims, wherein said correlator device is implemented as a low pass filter device arranged for performing an operation represented by the mathematical algorithm:
, wherein r is the output signal of the correlator device, X is the BM signal, s is the segment position, t is the time, T is the adjustable time shift and L is a low-pass filtering method
6. An apparatus as claimed in any one of claims 1-3, wherein said correlator device is implemented as a time normalised correlator device arranged for performing an operation represented by the mathematical algorithm:
R;,T(t) = L(xt(t),x,(t + T)) - 92 -
, herein R+ is the output signal, X is the BM signal, s is the segment position, t is the time, T is the adjustable time shift and L is a low -pass filtering method.
7. An apparatus as claimed in any one of the preceding claims, wherein said correlator device is implemented as a time normalised correlator device with a group delay corrector,
8. An apparatus as claimed in claim 7, wherein said time normal- ised correlator device with a group delay corrector is arranged for performing an operation represented by the mathematical algorithm:
R%.(t) = (xs(t + dI),x!(t + ds + T))
, wherein Red is the output signal, X is the BM signal, s is the segment position, t is the time, T is the adjustable time shift, L is the group delay as a function of the segment position and L is a low-pass filtering method.
9. An apparatus for determining the spectrum of an source signal including: a basilar membrane model device to which an input signal including the source signal is applied; and a correlator device connected to the basilar membrane model, which correlator device includes: a first input for receiving a BM signal which stems from a segment of the basilar membrane, for each of a multiple of segments of the basilar mem- brane; and at least one second input for receiving the BM signal shifted over an adjustable time shift T2, and which correlator device provides a segment-dependent output signal which forms a measure for an energy spectrum predominantly present in the source signal. - 93 -
10. An apparatus as claimed in claim 9 and any one of claims 3-8 , wherein the adjustable time T2 is set to depend on at least one common period predominantly present in the BM signals of the segments.
11. An apparatus as claimed in claim 10, wherein said adjustable time T2 is set to further depend on a segment dependent group delay of BM signals.
12. A apparatus for determining peaks in a signal including: a basilar membrane model device to which the signal is applied; a multiple of integrator devices, each integrator device being connected with an input to a segment of the basilar membrane model and for generating a excitation signal from the BM signal and transmitting the excitation signal to an output of the integrator device, a 3 dimensional matrix of the excitation signal by segment position by time forming a cochleogram ; a peak search device connected to the outputs of the integrator devices, for determining peaks in the excitation signals; in which apparatus a correlator device is connected to segments of the basi- lar membrane model and which correlator device is also communicatively connected to the peak search device, and to which correlator device the excitation signal of a segment of a selected position is applied, which selected position and correspondingly the segment varies in time depending on peaks determined by the peak search device.
13. An apparatus as claimed in claim 12, wherein said correlator device is implemented as a leaky autocorrelator device arranged for performing an operation represented by the mathematical algorithm:
R.( ,r( = £(*.ω ( . ,(,)(t - 0) - 94 -
, wherein r is the output signal of the correlator device, X is the BM signal, s is the segment position, t is the time, T is the adjustable time shift, and L is a low-pass filtering method.
14. An apparatus as claimed in any one of claims 12-13, wherein said correlator device is implemented as a time normalised correlator device arranged for performing an operation represented by the mathematical algorithm:
R;(rχτ(t) = L(xi(r)(t),xa(l)(t + T))
, wherein R+ is the output signal of the correlator device, X is the BM signal, s is the segment position, t is the time, T is the adjustable time shift, and L is a low-pass filtering method.
15. An apparatus as claimed in any one of claims 12-14, wherein said correlator device is implemented as a time normalised correlator with a group delay corrector
16. An apparatus as claimed in claim 14, wherein said time normalised correlator with a group delay corrector is arranged for performing an operation represented by the mathematical algorithm:
>.r( = x^it +d^x^ +d, + D)
, wherein RsA is the output signal of the correlator device, X is the BM signal, s is the segment position, t is the time, T is the adjustable time shift, dβ is the group delay as a function of the segment position and L is a low-pass filtering method. - 95 -
17. An apparatus as claimed in any one of the preceding claims, further including: a fpce device for fundamental period contour estimation, connected to the basilar membrane model device, the fpce device including: an input connected to the basilar membrane model; a ridge determination device, for determining estimated ridges and instan- taneous period contours; a ridge selector device connected to the ridge determination device, for selecting the most reliable smooth instantaneous period contours; an harmonic cloning device connected to the ridge selector device, for cloning of period contours to all possible fundamental periods, each combination of period contours of possible fundamental periods forming a contour hypothesis; a cloned contour selector device connected to the harmonic cloning device for selecting the most reliable period contours; a selector device connected to the cloned contour selector device for selecting at least one contour hypothesis that corresponds to a substantial part of the selected most reliable smooth instantaneous period contours; and an output connected to the selector device for transmitting the selected contour hypothesis further.
18. An apparatus as claimed in any one of the preceding claims, wherein said signal is a sound signal.
19. A signal recognition system including: an input; an apparatus as claimed in any one of claims 1-18; memory means connected to the device, the memory means containing data representing signals to be recognised; a processor device arranged for comparing a signal from the output of said apparatus with the signals to be recognised and determining a most similar signal most similar to the signal from the output of said apparatus from the signals to be recognised; an output.
20. A signal recognition system as claimed in claim 19, wherein said signals to be recognised represent speech signals.
21. A data compression syste including: an input; an apparatus as claimed in any of claims 1-18 connected to the input; processor means for reading the signal component values determined by said device of a signal received at the input of the device and transmitting the values to an output connected to the processor means.
22. A data expansion system including; an input for receiving signal component values determined with a system as claimed in claim 21, processsor means for reading the signal component values and reconstructing an original signal; an output for outputting the original signal.
23. A signal improvement system including: an input; a first apparatus as claimed in any one of claims 1-18; - 97 -
a masking device for selecting parts of a BM signal, the masking device being connected to an output of the first device; a second apparatus being substantially an inverse of the first apparatus for reconstructing a cochleogram of the selected parts of the BM signal, and having an input connected to the output of the masking device; an output connected to an output of the second device.
24. A signal improvement system as claimed in claim 22, wherein said masking device includes a coherent ridge estimation device for select- ing coherent ridges; a sine response adder device for replacing the selected coherent ridges with a sine response; an adder device for replacing a sine response with an original signal if the intensity of the sine response is lower than the intensity of the original sig- nal; a smoother device for removing discontinuities in the signal.
25. A method for estimating frequency characteristics of a source signal including the steps of: receiving said source signal at an input; simulating a response to said source signal of a basilar membrane having a number of segments, whereby an input signal is generated; generating at least one excitation signal of a basilar membrane segment from said input signal; whereby the 3 dimensional matrix of the excitation signal by time by segment forms a cochleogram; generating a shifted signal by shifting at least one of the at least one excitation signals with an adjustable time shift; combining at least one of the at least one excitation signals with said shifted signal, whereby a measure of the correlation between said excitation signal and the at least one of the at least one shifted signals is obtained. - 98 -
26. A method as claimed in claim 25, wherein said shifted signal is generated from an input signal and the shifted signal is combined with that same input signal.
27. A method as claimed in claim 26, wherein said shifted signal is generated from an input signal and the shifted signal is combined with that same input signal for each of a multiple of basilar membrane segments.
28. A method as claimed in any one of the claims 25-27, wherein said combining is performed by a leaky autocorrelation step including performing an operation represented by the mathematical algorithm:
_. = !, ..-- '„max
, wherein r is the output signal of the correlator device, X is the BM signal, s is the segment position, t is the time, T is the adjustable time shift and L is a low-pass filtering method.
29. A method as claimed in any one of the claims 25-27, wherein said combining is performed by a time normalised correlation step including per- forming an operation represented by the mathematical algorithm:
R r( = L(x,(t).x,(t + T
, herein R* is the output signal, X is the BM signal, s is the segment posi- tion, t is the time, T is the adjustable time shift and is a low-pass filtering method. - 99 -
30. A method as claimed in any one of the claims 25-29, wherein said combining is performed by a time normalised correlation step including a group delay correction step.
31. A method as claimed in claim 30, wherein said combining is performed by a time normalised correlation step including performing an operation represented by the mathematical algorithm:
R (t) = L(xI(t + dt)txt(t + da + T))
, wherein Red is the output signal, X is the BM signal, s is the segment posi- tion, t is the time, T is the adjustable time shift, α is the group delay as a function of the segment position and L is a low-pass filtering method.
32. A method as claimed in any one of claims 25-31 , wherein the adjustable time T2 is set to depend on at least one common period predomi- nantly present in the BM signals of the segments.
33. A method as claimed in claim 32, whereby after the step of generating at least one excitation signal, a step of determining a time by segment region of said cochleogram comprising a frequency component of said excita- tion signal is performed, and said time shift is adjusted depending on the determined region.
34. A method for determining peaks in a signal including: receiving said source signal at an input; simulating a response to said source signal of a basilar membrane having a number of segments, whereby an input signal is generated; generating at least one excitation signal of a basilar membrane segment from said input signal; determining peaks in the excitation signals; - 100 -
combining an excitation signal of a segment of a selected position, which selected position and correspondingly the segment varies in time depending on peaks determined.
35. A method as claimed in claim 34, wherein said combining is performed by a leaky autocorrelation step including performing an operation represented by the mathematical algorithm:
R.(,,.r( = (*,(,, (O. ' - ^>) , wherein r is the output signal of the correlator device, X is the BM signal, s is the segment position, t is the time, T is the adjustable time shift, and L is a low-pass filtering method.
36. A method as claimed in claim 34, wherein said combining is performed by a time normalised correlation step including performing an op- eration represented by the mathematical algorithm:
nAi) = χ 3U (t),χ i(l)(t + T))
, wherein R^ is the output signal of the correlator device, X is the BM signal, s is the segment position, t is the time, T is the adjustable time shift, and L is a low-pass filtering method,
37. A method as claimed in any one of claims 33-35, wherein said combining is implemented as a time normalised correlation step with a group delay correction
38. A method as claimed in claim 37, wherein said time normalised correlation with a group delay correction includes performing an operation represented by the mathematical algorithm: - 101 -
* r( = Ux,ι, it + df ),xi l)(t + J. + E))
, wherein ed is the output signal of the correlator device, X is the BM sig- nal, s is the segment position, t is the time, T is the adjustable time shift, d_ is the group delay as a function of the segment position and L is a low-pass filtering method.
39. A method as claimed in claims 25-37, further including: estimating a fundamental period contour estimation including: determining estimated ridges and instantaneous period contours; selecting the most reliable smooth instantaneous period contours; cloning of period contours to all possible fundamental periods, each combination of period contours of possible fundamental periods forming a contour hypothesis; selecting the most reliable period contours; selecting at least one contour hypothesis that corresponds to a substantial part of the selected most reliable smooth instantaneous period contours transmitting the selected contour hypothesis further.
40. A method as claimed in claims 25-38, wherein said signal is a sound signal.
41. A method for recognising signals including: a method as claimed in any one of claims 25-39; comparing an output signal with a signals to be recognised and determining from the signals to be recognised a most similar signal most similar to the signal. - 102 -
42. A method as claimed in claim 40, wherein said signals to be recognised represent speech signals.
43. A method for compressing data including: a method as claimed in any one of claims 25-43; reading the signal component values determined with said method of a signal received and transmitting the values further.
44. A method for expanding data including: receiving signal component values determined with a method as claimed in claim 44, reading the signal component values and reconstructing an original signal; outputting the original signal.
45. A method for improving a signal including: a first method as claimed in any one of claims 25-39; selecting parts of a BM signal; a second method substantially an inverse of the first method for reconstructing a cochleogram of the selected parts of the BM signal, using as an input the selected parts.
46. A method as claimed in claim 45, wherein said selecting includes selecting coherent ridges; replacing the selected coherent ridges with a sine response; replacing a sine response with an original signal if the intensity of the sine response is lower than the intensity of the original signal; removing discontinuities in the signal.
47. A method as claimed in any one of claims 25-46 wherein said sound signal contains speech from at least one speaker, - 103 -
48. A method as claimed in any one of claims 25-47, wherein said source signal is a unknown mixture of signals
49. A method as claimed in any one of claims 24-48, wherein after the step of selecting said detected signal, said detected signal is analysed further.
50. A computer program for running on a computer system, charac- terised in that the computer program contains code portions for performing steps of the methods as claimed in any one of claims 25-49 when ran on a computer system.
51. A data carrier containing data representing a computer program as is claimed in claim 50,
52 A device for estimating the frequency content of a sound signal which exhibits noise, comprising a basilar membrane model arranged for receiving the sound signal, which basilar membrane model comprises a number of series-connected segments, and a low -pass filter connected to the basilar membrane, which provides an estimated signal, characterised in that the low-pass filter is designed as a multiplier having a first and a second input, while to the first input is arranged for receiving a signal which stems from a segment of the basilar membrane and is present for a prede- termined period of time, and to the second input is arranged for receiving the signal shifted over an adjustable time Tl, and in that the multiplier provides a tune Tl_dependent output signal which depends on the -frequency substantially present in the signal of that segment and forms a measure for the frequency content of the sound signal. - 104 -
53. A device for estimating the spectrum of a sound signal which exhibits noise, comprising a basilar membrane model arranged for receiving the sound signal, which model comprises a number of series-connected segments, and a low-pass filter connected to the basilar membrane, which pro- vides an estimated signal, characterised in that the low-pass filter is designed as a multiplier having a first and a second input, wherein in use for each segment of the basilar membrane, to the first input a signal is applied which stems from that segment and to the second input said signal is applied shifted over a time T2, and that the multiplier provides a segment- dependent output signal which forms a measure for a firequency energy spectrum substantially present in the sound signal.
PCT/NL2000/000808 1999-11-05 2000-11-06 Methods and apparatuses for signal analysis WO2001033547A1 (en)

Priority Applications (6)

Application Number Priority Date Filing Date Title
US10/129,460 US6745155B1 (en) 1999-11-05 2000-11-06 Methods and apparatuses for signal analysis
CA2390244A CA2390244C (en) 1999-11-05 2000-11-06 Methods and apparatuses for signal analysis
DE60033549T DE60033549T2 (en) 1999-11-05 2000-11-06 METHOD AND DEVICE FOR SIGNAL ANALYSIS
EP00980108A EP1228502B1 (en) 1999-11-05 2000-11-06 Methods and apparatuses for signal analysis
JP2001535156A JP4566493B2 (en) 1999-11-05 2000-11-06 Signal analysis method and apparatus
AU17408/01A AU1740801A (en) 1999-11-05 2000-11-06 Methods and apparatuses for signal analysis

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
NL1013500A NL1013500C2 (en) 1999-11-05 1999-11-05 Apparatus for estimating the frequency content or spectrum of a sound signal in a noisy environment.
NL1013500 1999-11-05

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WO2001033547A1 WO2001033547A1 (en) 2001-05-10
WO2001033547B1 true WO2001033547B1 (en) 2001-11-29

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EP (1) EP1228502B1 (en)
JP (1) JP4566493B2 (en)
CN (1) CN1286084C (en)
AT (1) ATE354849T1 (en)
AU (1) AU1740801A (en)
CA (1) CA2390244C (en)
DE (1) DE60033549T2 (en)
NL (1) NL1013500C2 (en)
WO (1) WO2001033547A1 (en)

Families Citing this family (38)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1244093B1 (en) * 2001-03-22 2010-10-06 Panasonic Corporation Sound features extracting apparatus, sound data registering apparatus, sound data retrieving apparatus and methods and programs for implementing the same
US7136813B2 (en) * 2001-09-25 2006-11-14 Intel Corporation Probabalistic networks for detecting signal content
FR2834363B1 (en) * 2001-12-27 2004-02-27 France Telecom METHOD FOR CHARACTERIZING A SOUND SIGNAL
US7065485B1 (en) * 2002-01-09 2006-06-20 At&T Corp Enhancing speech intelligibility using variable-rate time-scale modification
US7376553B2 (en) * 2003-07-08 2008-05-20 Robert Patel Quinn Fractal harmonic overtone mapping of speech and musical sounds
US7672834B2 (en) * 2003-07-23 2010-03-02 Mitsubishi Electric Research Laboratories, Inc. Method and system for detecting and temporally relating components in non-stationary signals
US7522961B2 (en) 2004-11-17 2009-04-21 Advanced Bionics, Llc Inner hair cell stimulation model for the use by an intra-cochlear implant
US7242985B1 (en) * 2004-12-03 2007-07-10 Advanced Bionics Corporation Outer hair cell stimulation model for the use by an intra—cochlear implant
US7742914B2 (en) * 2005-03-07 2010-06-22 Daniel A. Kosek Audio spectral noise reduction method and apparatus
US20060206320A1 (en) * 2005-03-14 2006-09-14 Li Qi P Apparatus and method for noise reduction and speech enhancement with microphones and loudspeakers
JP2006309162A (en) * 2005-03-29 2006-11-09 Toshiba Corp Pitch pattern generating method and apparatus, and program
KR100724736B1 (en) * 2006-01-26 2007-06-04 삼성전자주식회사 Method and apparatus for detecting pitch with spectral auto-correlation
US7729775B1 (en) 2006-03-21 2010-06-01 Advanced Bionics, Llc Spectral contrast enhancement in a cochlear implant speech processor
US8949120B1 (en) 2006-05-25 2015-02-03 Audience, Inc. Adaptive noise cancelation
US8311634B2 (en) * 2006-06-16 2012-11-13 Second Sight Medical Products Inc. Apparatus and method for electrical stimulation of human retina
US8457754B2 (en) * 2006-06-16 2013-06-04 Second Sight Medical Products, Inc. Apparatus and method for electrical stimulation of human neurons
US7995771B1 (en) 2006-09-25 2011-08-09 Advanced Bionics, Llc Beamforming microphone system
US7864968B2 (en) * 2006-09-25 2011-01-04 Advanced Bionics, Llc Auditory front end customization
US10319313B2 (en) * 2007-05-21 2019-06-11 E Ink Corporation Methods for driving video electro-optic displays
EP2028651A1 (en) * 2007-08-24 2009-02-25 Sound Intelligence B.V. Method and apparatus for detection of specific input signal contributions
EP2261681A4 (en) * 2008-04-04 2014-11-05 Anritsu Corp Method of detecting fundamental wave beat component, sampling device for measured signal using the same, and waveform observation system
KR20090122143A (en) * 2008-05-23 2009-11-26 엘지전자 주식회사 A method and apparatus for processing an audio signal
EP2329399A4 (en) * 2008-09-19 2011-12-21 Newsouth Innovations Pty Ltd Method of analysing an audio signal
US8359195B2 (en) * 2009-03-26 2013-01-22 LI Creative Technologies, Inc. Method and apparatus for processing audio and speech signals
US20110178800A1 (en) * 2010-01-19 2011-07-21 Lloyd Watts Distortion Measurement for Noise Suppression System
CN101806835B (en) * 2010-04-26 2011-11-09 江苏中凌高科技有限公司 Interharmonics measuring meter based on envelope decomposition
US9558755B1 (en) 2010-05-20 2017-01-31 Knowles Electronics, Llc Noise suppression assisted automatic speech recognition
US20120143611A1 (en) * 2010-12-07 2012-06-07 Microsoft Corporation Trajectory Tiling Approach for Text-to-Speech
US20120197643A1 (en) * 2011-01-27 2012-08-02 General Motors Llc Mapping obstruent speech energy to lower frequencies
US9640194B1 (en) 2012-10-04 2017-05-02 Knowles Electronics, Llc Noise suppression for speech processing based on machine-learning mask estimation
KR102212225B1 (en) * 2012-12-20 2021-02-05 삼성전자주식회사 Apparatus and Method for correcting Audio data
US9536540B2 (en) 2013-07-19 2017-01-03 Knowles Electronics, Llc Speech signal separation and synthesis based on auditory scene analysis and speech modeling
EP2963649A1 (en) 2014-07-01 2016-01-06 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Audio processor and method for processing an audio signal using horizontal phase correction
DE112015003945T5 (en) 2014-08-28 2017-05-11 Knowles Electronics, Llc Multi-source noise reduction
US9980046B2 (en) * 2016-09-29 2018-05-22 Invensense, Inc. Microphone distortion reduction
CN109540545B (en) * 2018-11-30 2020-04-14 厦门大学 Abnormal sound diagnosis signal acquisition device and processing method for power output assembly of tractor
CN112763980B (en) * 2020-12-28 2022-08-05 哈尔滨工程大学 Target motion analysis method based on azimuth angle and change rate thereof
US11830481B2 (en) * 2021-11-30 2023-11-28 Adobe Inc. Context-aware prosody correction of edited speech

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3622706A (en) * 1969-04-29 1971-11-23 Meguer Kalfaian Phonetic sound recognition apparatus for all voices
DE3070698D1 (en) * 1979-05-28 1985-07-04 Univ Melbourne Speech processor
CA1189147A (en) * 1980-12-12 1985-06-18 James F. Patrick Speech processors
US5473759A (en) * 1993-02-22 1995-12-05 Apple Computer, Inc. Sound analysis and resynthesis using correlograms
US6072885A (en) * 1994-07-08 2000-06-06 Sonic Innovations, Inc. Hearing aid device incorporating signal processing techniques
EP0852708B1 (en) * 1995-09-29 2001-09-12 International Business Machines Corporation Mechanical signal processor based on micromechanical oscillators and intelligent acoustic detectors and systems based thereon
US5856722A (en) * 1996-01-02 1999-01-05 Cornell Research Foundation, Inc. Microelectromechanics-based frequency signature sensor
US5879283A (en) * 1996-08-07 1999-03-09 St. Croix Medical, Inc. Implantable hearing system having multiple transducers
US6501399B1 (en) * 1997-07-02 2002-12-31 Eldon Byrd System for creating and amplifying three dimensional sound employing phase distribution and duty cycle modulation of a high frequency digital signal
EP0980064A1 (en) * 1998-06-26 2000-02-16 Ascom AG Method for carrying an automatic judgement of the transmission quality of audio signals

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CN1286084C (en) 2006-11-22
JP4566493B2 (en) 2010-10-20
ATE354849T1 (en) 2007-03-15
AU1740801A (en) 2001-05-14
EP1228502A1 (en) 2002-08-07
WO2001033547A1 (en) 2001-05-10
CA2390244C (en) 2011-07-19
CA2390244A1 (en) 2001-05-10
DE60033549D1 (en) 2007-04-05
CN1421030A (en) 2003-05-28
JP2003513339A (en) 2003-04-08
DE60033549T2 (en) 2007-11-22
EP1228502B1 (en) 2007-02-21
US6745155B1 (en) 2004-06-01
NL1013500C2 (en) 2001-05-08

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